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Intfloat · Embedding model
E5-Large-v2
intfloat/e5-large-v2- Embedding
The e5-large-v2 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-accuracy semantic embeddings optimized for retrieval, semantic search, reranking, and similarity-scoring tasks.
Specifications
- Input
- text
- Output
- vector per input (float array; base64 on request)
- Batch
- up to 512 inputs per call
- Billing
- per input token
Use it
POST /api/v1/embeddings · MCP embed · full reference
curl https://omnirail.org/api/v1/embeddings \
-H "Authorization: Bearer $OMNIRAIL_KEY" -H "Content-Type: application/json" \
-d '{"model":"intfloat/e5-large-v2","input":["brass lever espresso machine","coffee grinder"]}'